The subject matter disclosed herein relates generally to systems and methods for computed tomography (CT) imaging.
In CT imaging, an X-ray source may be rotated around an object to obtain imaging information. X-rays from the source attenuated by the object may be collected or detected by a detector and used to reconstruct an image. Due to variations in attenuation as a function of energy level among materials, spectral CT imaging provides the ability to distinguish different materials even if the materials have similar attenuation for conventional single-energy CT scans at a given energy. Spectral CT imaging may be used to provide synthetic monochromatic images using linear combinations of material decomposed (MD) images. However, the raw MD images (i.e., water and iodine), as the direct results of filtered back-projection, may suffer significant amounts of negatively correlated noise resulting from projection-space material decomposition. Subsequent steps for noise reduction may thus be required. At some energies, one or the other of the MD images may tend to dominate, while at other energies the negatively correlated noise may essentially be cancelled. When one of the MD images tends to dominate, additional noise reduction may be required to keep noise at acceptable levels in synthesized monochromatic images while preserving spatial resolution and image quality (IQ).
Conventional noise reduction approaches may not achieve consistently lower noise in synthetic monochromatic images across all photon energy levels, as compared to raw MD images. For example, if conventionally de-noised MD images are combined, the noise of the resulting monochromatic image may contain more noise than the combination of raw MD images for a range of photon energies. Raw and de-noised images may be blended when producing monochromatic images, in order to select which MD images (raw or de-noised) should be combined to produce lower noise across an entire energy range. However, such blending results in unnecessarily large memory usage and storage requirements, as the information to de-noise images on-the-fly is stored along with the raw MD image information. Further, conventional de-noising approaches may not achieve sufficient noise reduction at low dosage levels, and/or may introduce artifacts that compromise image texture and spatial resolution.